“Computational Fluid Dynamics”, also known as CFD for short, is a method for simulating the blood flow in a vascular section or vascular segment of a blood vessel which contains a pathological, i.e. a morbid change. Such a pathological change in the vascular section exists for example in the form of an aneurysm, i.e. a morbid, locally delimited, frequently bag-like enlargement. An aneurysm can occur in particular in a blood vessel in the region of the brain or of the heart; however, the occurrence of an aneurysm is generally not restricted to a specific region of the body. The clinical significance of an aneurysm, which for example is localized in the brain, arises in particular from the risk of a rupture, i.e. the formation of a tear or burst, which for example can result in hemorrhages and thromboses. In modern medicine, the dynamics of the blood flow in an aneurysm are frequently considered to be a major factor in the pathogenesis of the aneurysm, i.e. in its formation and development.
This simulation of the blood flow by CFD methods imparts a three-dimensional distribution of the flow parameters, such as for example WSS (Wall Shear Stress), along the surface of the vascular lumen.
DE 10 2008 014 792 B3 describes such a method for simulating a blood flow in a vascular section, wherein a captured image of a vascular region including the vascular section is obtained, a 3D vascular section model is determined from the captured image, a number of blood flow parameters are read in, the blood flow is simulated in the vascular section model with the inclusion of the or every blood flow parameter and a number of hemodynamic parameters are output. It is here provided that the captured image is obtained with an implant used in the vascular section such that image data of the implant is included, and that the 3D vascular section model is determined having regard to the image data of the implant used. Furthermore, a corresponding apparatus for simulating a blood flow in a vascular section is specified.
As is known from the article “Image-Based Computational Simulation of Flow Dynamics in a Giant Intracranial Aneurysm” by D. A. Steinmann et al. [1], a number of what are known as hemodynamic parameters are related to a growth and a burst of the aneurysm. A hemodynamic parameter is understood in particular as a parameter which relates to the hemodynamics, i.e. fluid mechanics, of the blood. In the cited article a pressure, a stress and shear stress affecting the vascular wall, as well as a flow rate, are mentioned among other things as hemodynamic parameters. In order to extrapolate such hemodynamic parameters, the blood flow in a vascular section which for example includes the aneurysm, is for example simulated.
In this article by D. A. Steinmann et al. [1] a 3D vascular section model is determined to this end from a 3D captured image which was obtained by means of rotational angiography. The blood flow in the 3D vascular section model is simulated using the CFD method. The simulation is performed here on the assumption of rigid vascular walls and a constant blood viscosity. CFD is a method of numeric flow simulation. The model equations used in numeric fluid mechanics are mostly based on a Navier-Stokes equation, on an Euler equation or on a potential equation.
This method of blood flow simulation is currently being employed in a number of experimental studies. A major restriction is that in this specific application on humans not all basic conditions necessary for the simulation are sufficiently precisely known on an individual patient basis. Hence it is difficult to validate the method and in the past this has not been done.
This means that the resulting flow results for the individual patient may be incorrect. Important basic conditions here include the geometry of the vascular section with aneurysm, the inflow and outflow values of the blood which change over time (speed, volume, etc.), the characteristics of the blood and the local elastic characteristics of the vascular wall.
To perform such rotational angiography to generate 3D captured images in order to obtain a 3D vascular section model, use is made of X-ray systems, the typical important features of which can for example be at least one C-arm, which can be robot-controlled and to which an X-ray tube and a radiographic image detector are attached, a patient support table, a high-voltage generator for generating the tube voltage, a system control unit and an imaging system including at least one monitor.
Such a typical X-ray system with a robot-mounted C-arm shown as an example in FIG. 1 for example has a C-arm 2 rotatably mounted on a stand in the four of a six-axis industrial or buckling arm robot 1, with an X-ray radiation source, for example a radiographic tube unit 3 with X-ray tube and collimator, and a radiographic image detector 4 as an image capturing unit being attached to the ends of said C-arm 2.
In general CTA and MRA are also suitable for generating the 3D models. The advantage with C-arm systems is that if necessary the 2D recordings are intrinsically registered. Otherwise this has to be done for all modalities.
Using for example the buckling aim robot 1 known from U.S. Pat. No. 7,500,784 B2 which preferably has six rotary axes and thus six degrees of freedom, the C-arm 2 can be spatially readjusted at will, for example by being rotated about a center of rotation between the radiographic tube unit 3 and the radiographic image detector 4. The inventive X-ray system 1 to 4 can in particular be rotated about centers of rotation and rotary axes in the C-arm plane of the radiographic image detector 4, preferably about the center point of the radiographic image detector 4 and about rotary axes intersecting the center point of the radiographic image detector 4.
The known buckling arm robot 1 has a base frame which for example is permanently mounted on a floor. Attached to this is a carrousel which can rotate about a first rotary axis. Fixed to the carrousel is a robot swing arm which can swivel about a second rotary axis, to which is attached a robot arm which can rotate about a third rotary axis. Fixed to the end of the robot arm is a robot hand which can rotate about a fourth rotary axis. The robot hand has a fixing element for the C-arm 2 which can swivel about a fifth rotary axis and can rotate about a sixth axis of rotation running perpendicular thereto.
The implementation of the radiographic diagnostic device is not reliant on the industrial robot. Standard C-arm devices can also be used.
The radiographic image detector 4 can be a rectangular or quadratic, flat semiconductor detector which is preferably made of amorphous silicon (a-Si). However, integrating and possibly metering CMOS detectors can also be used.
In the beam path of the radiographic tube unit 3 a patient 6 to be examined is placed on a patient support table 5 as an examination object for recording a heart for example. Connected to the radiographic diagnostic device is a system control unit 7 with an image system 8 which receives and processes the image signals from the radiographic image detector 4 (operating elements are for example not shown). The X-ray images can then be viewed on displays of a bank of monitors 9.
In the methods currently used for blood flow simulation not all basic conditions necessary for the simulation are sufficiently precisely known on an individual patient basis in this specific application on humans. Hence it is difficult to validate the method. In the references cited below, different approaches are mentioned which enable CFD simulations to be validated.
In “Blood flow in cerebral aneurysm: Comparison of phase contrast magnetic resonance and computational fluid dynamics—preliminary results” by Karmonik et al. [2] the result of a CFD simulation is compared to MR. The MR measurement itself is however imprecise because of the limited resolution and requires a considerable investment in time. Moreover this examination is virtually never performed on patients.
In “Methodologies to assess blood flow in cerebral aneurysm: Current state of research and perspectives” by Augsburger et al. [3] a procedure using in-vitro transparent vascular models and “Particle Image Velocimetry” (PIV) is described which permits CDF data to be compared to measured data.
However, neither method is suitable for validating the CFD measurement for the individual patient.
In “Quantitative evaluation of virtual angiography for interventional X-ray acquisitions” by Sun et al. [4] a method is described which is suitable for verifying CFD simulations for the individual patient in a particular way. To this end the CFD simulation is used to create a virtual angiography which can be compared to a genuine angiography recording of the patient. The result is a qualitative comparison in 2D. A description is additionally given of how to generate a center line and create a flow map along this center line in both angiography recordings. These two lines can be used to perform a quantitative comparison in 1D (along the line), which can be specified in the form of a relative mean quadratic error.